A Data‐Driven‐Based Parameters Estimation Approach for a Modern Power System Frequency Response Model

The integration growth of grid‐forming (GFM) and grid‐following (GFL) converters into the power system necessitates the frequency response model (FRM) improvement. This paper estimates the unknown important parameters in an updated FRM using the least‐square method (LSM). To estimate the parameters, firstly, the moment of inertia of the generation units is estimated using the acquired data comprising the active power deviation, frequency deviation, and the rate of change of frequency. Secondly, the acquired data from the units within control areas are used to compute the total moment of inertia of each control area, the related frequency deviation, and the RoCoF. In the third step, the value of the interfacing parameter between the synchronous generation area and GFM area, in the updated FRM is calculated using the LSM. Afterward, the value of the synchronizing parameter between the synchronous generation area and GFL area is obtained based on the calculated parameter in the previous step. Finally, the parameters included in the aggregated GFL (AGFL) system are estimated using the collected data and the least‐square concept. Moreover, the estimated parameters using the neural network is discussed and compared with the LSM‐based approach in this paper. Some simulations are applied to the IEEE 39‐bus power system in the MATLAB/Simulink environment, and the results are compared with the response of the updated FRM using the estimated parameters to verify the accuracy of the proposed approach.